Anjus George joined ORNL as an HPC Systems Software Engineer with the Technology Integration Group at the National Center for Computational Sciences (NCCS). She is responsible for Lustre code development and production support to NCCS programs. Anjus received her PhD in Computer Science from University of North Carolina at Charlotte (UNCC) in 2020. Her areas of focus were distributed systems, Internet of Things (IoT) and Edge Computing. During her PhD she worked as research assistant and teaching assistant at UNCC.

Prior to joining her PhD, Anjus worked as a Software Engineer at Robert Bosch where she developed engine management system software for European automobiles. She obtained her Master’s degree in VLSI and Embedded Systems from Cochin University of Science and Technology (CUSAT), India in 2014.


University of North Carolina at Charlotte
Computer Science
Doctor of Philosophy (Ph.D.)
Cochin University of Science and Technology
VLSI and Embedded Systems
Master of Science (M.S.)
Cochin University of Science and Technology
Electronics and Communication Engineering
Bachelor of Science (B.S.)

Staff Activities

Committee Activity:


ATS Seminar Series

Series of seminars organized in Advanced Technology Section.


A. George, A. Ravindran, M. Mendieta and H. Tabkhi, "Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge," in IEEE Access, vol. 9, pp. 21457-21473, 2021, doi: 10.1109/ACCESS.2021.3055775.
George, Anjus, Mohr, Rick, Simmons, James, and Oral, Sarp. 2021. "Understanding Lustre Internals Second Edition". United States.
George A., Ravindran A. (2021) Scalable Approximate Computing Techniques for Latency and Bandwidth Constrained IoT Edge. In: Paiva S., Lopes S.I., Zitouni R., Gupta N., Lopes S.F., Yonezawa T. (eds) Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 372. Springer, Cham.
George A., Ravindran A. (2019) Latency Control for Distributed Machine Vision at the Edge Through Approximate Computing. In: Zhang T., Wei J., Zhang LJ. (eds) Edge Computing – EDGE 2019. EDGE 2019. Lecture Notes in Computer Science, vol 11520. Springer, Cham.